Since World War II, health care spending in the United States has grown rapidly – faster than in the rest of the developed world, and much faster than the US gross domestic product. With less than 5% of the world’s population, the US now accounts for 40% of global health spending, almost twice as much per capita as the next highest-spending country. American health outcomes, however, have not kept pace with spending. The United States compares remarkably poorly to other developed countries on such measures as life expectancy at birth, infant mortality, and other indicators of population health. In other words, we are spending more and more on health care for lower and lower returns in the form of better health. Despite a steady stream of medical innovations, productivity growth in the health care sector has been slow.
There are several reasons for this poor productivity. Many tests and treatments (both new and old) are routinely put to use with little or no regard for whether they improve patient outcomes. Treating prostate cancer with proton beam therapy, for example, costs $50,000 per patient – roughly twice the cost of standard radiation treatment. Yet there is no evidence that proton beam therapy is any better for the patient’s chances of surviving cancer or avoiding serious side effects.
There is also evidence that between one-tenth and one-third of tests and treatments are unnecessary or unwanted by patients. Such overuse consumes real resources and can cause real harm, and is largely the result of two main factors. One is the failure to measure whether treatments such as proton beam therapy are effective. The other is a phenomenon known as “supply-sensitive” care, the tendency of providers to deliver hospitalizations and other medical services simply because resources such as beds and technology are available.
There is one additional reason for health care’s poor productivity: medical institutions are poorly organized. They waste time, money, labor, and other resources by operating inefficiently. The Dartmouth Atlas, which tracks Medicare data, shows that inputs (beds, physicians, equipment per capita) vary widely among hospitals, with little if any discernible difference in the outcomes of statistically similar patient populations.
Studies of provider performance, especially of hospitals, suggest that vast productivity gains are within reach of American medicine. Some institutions have made dramatic improvements in efficiency by improving their care delivery processes – improvements that have led, for example, to a 30% reduction in the use of MRIs for patients with back pain, and improvements in workflow patterns that take nurses from spending over 60% of their time away from patients to spending 90% of their time with patients. For example, if all hospitals achieved the same level of efficiency for inpatient care as Intermountain Healthcare, a multi-hospital chain based in Salt Lake City, total hospital spending in the US would fall by an estimated 43 percent.
Making better use of health care labor force is the key to improving productivity in the sector. This paper looks first at sources of low productivity in health care, and then examines the implications for future health care workforce needs.